Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	Louisiana	34,388
2	Florida	31,250
3	Mississippi	30,655
4	Arizona	28,839
5	Alabama	28,774
6	Georgia	26,594
7	South Carolina	26,049
8	Tennessee	25,537
9	Texas	24,349
10	Iowa	24,303
11	Nevada	24,147
12	Arkansas	23,930
13	New York	23,187
14	New Jersey	22,470
15	Rhode Island	22,049
16	North Dakota	21,436
17	Illinois	21,250
18	District of Columbia	20,889
19	Idaho	20,477
20	Nebraska	20,377
21	Delaware	19,752
22	Maryland	19,582
23	California	19,571
24	South Dakota	19,545
25	Utah	18,643
26	Oklahoma	18,267
27	Massachusetts	18,237
28	North Carolina	18,001
29	Kansas	17,789
30	Missouri	17,717
31	Wisconsin	16,917
32	Indiana	16,343
33	Virginia	15,975
34	Connecticut	15,473
35	Minnesota	15,225
36	Kentucky	14,039
37	New Mexico	12,896
38	Michigan	12,594
39	Ohio	12,021
40	Puerto Rico	11,987
41	Pennsylvania	11,864
42	Washington	11,124
43	Colorado	10,951
44	Alaska	9,989
45	Montana	8,876
46	Wyoming	7,889
47	Hawaii	7,805
48	West Virginia	7,363
49	Oregon	7,078
50	New Hampshire	5,721
51	Maine	3,675
52	Vermont	2,727

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	North Dakota	330
2	Kansas	277
3	Tennessee	248
4	Oklahoma	246
5	South Dakota	246
6	Missouri	233
7	Arkansas	220
8	Wisconsin	216
9	Nebraska	212
10	Iowa	211
11	Utah	195
12	Texas	185
13	Idaho	168
14	Alabama	158
15	Kentucky	157
16	Mississippi	152
17	South Carolina	146
18	Georgia	145
19	Rhode Island	142
20	Montana	140
21	Delaware	131
22	Illinois	129
23	Wyoming	126
24	Florida	111
25	North Carolina	110
26	Louisiana	105
27	Indiana	99
28	Virginia	99
29	Maryland	98
30	Puerto Rico	94
31	West Virginia	92
32	Michigan	90
33	Minnesota	88
34	Ohio	88
35	Pennsylvania	84
36	California	83
37	Colorado	82
38	Alaska	78
39	Connecticut	78
40	Nevada	78
41	District of Columbia	71
42	Washington	67
43	Arizona	63
44	Hawaii	57
45	New Mexico	44
46	New Jersey	43
47	Massachusetts	42
48	Oregon	41
49	New York	34
50	New Hampshire	20
51	Maine	19
52	Vermont	9

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,807
2	New York	1,678
3	Massachusetts	1,341
4	Connecticut	1,258
5	Louisiana	1,138
6	Rhode Island	1,020
7	Mississippi	926
8	District of Columbia	874
9	Arizona	738
10	Michigan	695
11	Illinois	680
12	Maryland	637
13	Delaware	635
14	Pennsylvania	622
15	South Carolina	608
16	Florida	602
17	Georgia	591
18	Indiana	515
19	Texas	510
20	Alabama	487
21	Nevada	485
22	New Mexico	396
23	Iowa	395
24	Ohio	389
25	Arkansas	383
26	California	372
27	Minnesota	351
28	Colorado	349
29	Virginia	337
30	New Hampshire	322
31	Tennessee	311
32	North Carolina	302
33	Missouri	299
34	Washington	277
35	Kentucky	250
36	Idaho	240
37	North Dakota	236
38	Nebraska	235
39	Oklahoma	233
40	South Dakota	217
41	Wisconsin	212
42	Kansas	201
43	Puerto Rico	178
44	West Virginia	161
45	Utah	136
46	Montana	131
47	Oregon	123
48	Maine	102
49	Vermont	92
50	Wyoming	79
51	Hawaii	72
52	Alaska	56

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Arkansas	19
2	Mississippi	6
3	Virginia	6
4	Florida	5
5	Kansas	5
6	Louisiana	4
7	Nevada	4
8	South Carolina	4
9	West Virginia	4
10	North Carolina	3
11	North Dakota	3
12	Ohio	3
13	Puerto Rico	3
14	Rhode Island	3
15	South Dakota	3
16	Tennessee	3
17	Texas	3
18	Alabama	2
19	Arizona	2
20	California	2
21	Georgia	2
22	Idaho	2
23	Iowa	2
24	Wyoming	2
25	Delaware	1
26	Illinois	1
27	Indiana	1
28	Kentucky	1
29	Massachusetts	1
30	Michigan	1
31	Missouri	1
32	Montana	1
33	Nebraska	1
34	New Mexico	1
35	Oklahoma	1
36	Pennsylvania	1
37	Washington	1
38	Wisconsin	1
39	Alaska	0
40	Colorado	0
41	Connecticut	0
42	District of Columbia	0
43	Hawaii	0
44	Maine	0
45	Maryland	0
46	Minnesota	0
47	New Hampshire	0
48	New Jersey	0
49	New York	0
50	Oregon	0
51	Utah	0
52	Vermont	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Chattahoochee	Georgia	146,328	1	99
Trousdale	Tennessee	145,693	2	99
Lafayette	Florida	145,690	3	99
Lincoln	Arkansas	138,360	4	99
Lake	Tennessee	123,147	5	99
Davidson	Tennessee	39,708	172	94
Richland	South Carolina	33,531	271	91
York	South Carolina	17,592	1061	66
Orange	California	16,527	1161	63
Pierce	Washington	9,093	2050	34

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Hancock	Georgia	4,848	1	99
Galax city	Virginia	4,727	2	99
Emporia city	Virginia	4,302	3	99
Randolph	Georgia	4,131	4	99
Terrell	Georgia	3,634	5	99
Richland	South Carolina	529	700	77
Davidson	Tennessee	409	946	69
Orange	California	350	1090	65
Pierce	Washington	231	1466	53
York	South Carolina	221	1513	51

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.